Built a Predictive Incident Response Agent with LLMs and Vector Memory

The author built an AI-powered Incident Response Agent that combines Large Language Models (LLMs) and vector memory to help engineering teams resolve production issues faster by remembering past incidents and predicting future failures.

Details

The Predictive Incident Intelligence System is designed to be a proactive assistant during production outages. It leverages Groq LLM for fast inference and log analysis, Hindsight vector memory to store and recall past incidents, and a FastAPI backend to handle log streams and coordinate the agent's logic. The system follows a workflow of ingesting and parsing logs, retrieving historical context from vector memory, using the LLM to identify root causes and resolution steps, and then predicting potential secondary failures. The results are surfaced in a Streamlit-based interactive dashboard where engineers can view insights and chat with the agent. Key architectural challenges included implementing strict environment-based configuration and ensuring scalability and reliability.